Quintel, Michael I.

[["dc.bibliographiccitation.firstpage","2187"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Intensive Care Medicine"],["dc.bibliographiccitation.lastpage","2196"],["dc.bibliographiccitation.volume","46"],["dc.contributor.author","Chiumello, Davide"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Coppola, Silvia"],["dc.contributor.author","Romitti, Federica"],["dc.contributor.author","Formenti, Paolo"],["dc.contributor.author","Bonifazi, Matteo"],["dc.contributor.author","Pozzi, Tommaso"],["dc.contributor.author","Palumbo, Maria Michela"],["dc.contributor.author","Cressoni, Massimo"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Camporota, Luigi"],["dc.contributor.author","Marini, John J."],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2021-04-14T08:32:14Z"],["dc.date.available","2021-04-14T08:32:14Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/s00134-020-06281-2"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83854"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1432-1238"],["dc.relation.issn","0342-4642"],["dc.title","Physiological and quantitative CT-scan characterization of COVID-19 and typical ARDS: a matched cohort study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","15"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Respiratory Care"],["dc.bibliographiccitation.lastpage","22"],["dc.bibliographiccitation.volume","61"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Harnisch, Lars-Olav"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Zippel, Carsten"],["dc.contributor.author","Quintel, Michael"],["dc.date.accessioned","2018-11-07T10:21:44Z"],["dc.date.available","2018-11-07T10:21:44Z"],["dc.date.issued","2016"],["dc.description.abstract","BACKGROUND: During noninvasive ventilation (NIV) of COPD patients, delayed off-cycling of pressure support can cause patient ventilator mismatch and NIV failure. This systematic experimental study analyzes the effects of varying cycling criteria on patient-ventilator interaction. METHODS: A lung simulator with COPD settings was connected to an ICU ventilator via helmet or face mask. Cycling was varied between 10 and 70% of peak inspiratory flow at different breathing frequencies (15 and 30 breaths/min) and pressure support levels (5 and 15 cm H2O) using the ventilator's invasive and NIV mode with and without an applied leakage. RESULTS: Low cycling criteria led to severe expiratory cycle latency. Augmenting off-cycling reduced expiratory cycle latency (P < .001), decreased intrinsic PEEP, and avoided non-supported breaths. Setting cycling to 50% of peak inspiratory flow achieved best synchronization. Overall, using the helmet interface increased expiratory cycle latency in almost all settings (P < .001). Augmenting cycling from 10 to 40% progressively decreased expiratory pressure load (P < .001). NIV mode decreased expiratory cycle latency compared with the invasive mode (P < .001). CONCLUSION: Augmenting the cycling criterion above the default setting (20-30% peak inspiratory flow) improved patient ventilator synchrony in a simulated COPD model. This suggests that an individual approach to cycling should be considered, since interface, level of pressure support, breathing frequency, and leakage influence patient-ventilator interaction and thus need to be considered."],["dc.identifier.doi","10.4187/respcare.04141"],["dc.identifier.isi","000367062300005"],["dc.identifier.pmid","26556898"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42144"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Daedalus Enterprises Inc"],["dc.relation.issn","1943-3654"],["dc.relation.issn","0020-1324"],["dc.title","Patient-Ventilator Interaction During Noninvasive Ventilation in Simulated COPD"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","R165"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","CRITICAL CARE"],["dc.bibliographiccitation.lastpage","R171"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Rylander, C."],["dc.contributor.author","Tylen, U."],["dc.contributor.author","Rossi-Norrlund, R."],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Quintel, M."],["dc.contributor.author","Bake, B."],["dc.date.accessioned","2018-11-07T11:10:20Z"],["dc.date.available","2018-11-07T11:10:20Z"],["dc.date.issued","2005"],["dc.description.abstract","Introduction The aim of this study was to assess the volume of gas being poorly ventilated or non-ventilated within the lungs of patients treated with mechanical ventilation and suffering from acute respiratory distress syndrome (ARDS). Methods A prospective, descriptive study was performed of 25 sedated and paralysed ARDS patients, mechanically ventilated with a positive end-expiratory pressure (PEEP) of 5 cmH(2)O in a multidisciplinary intensive care unit of a tertiary university hospital. The volume of poorly ventilated or non-ventilated gas was assumed to correspond to a difference between the ventilated gas volume, determined as the end-expiratory lung volume by rebreathing of sulphur hexafluoride (EELVSF6), and the total gas volume, calculated from computed tomography images in the end-expiratory position (EELVCT). The methods used were validated by similar measurements in 20 healthy subjects in whom no poorly ventilated or non-ventilated gas is expected to be found. Results EELVSF6 was 66% of EELVCT, corresponding to a mean difference of 0.71 litre. EELVSF6 and EELVCT were significantly correlated (r(2) = 0.72; P < 0.001). In the healthy subjects, the two methods yielded almost identical results. Conclusion About one-third of the total pulmonary gas volume seems poorly ventilated or non-ventilated in sedated and paralysed ARDS patients when mechanically ventilated with a PEEP of 5 cmH2O. Uneven distribution of ventilation due to airway closure and/or obstruction is likely to be involved."],["dc.identifier.doi","10.1186/cc3058"],["dc.identifier.isi","000227588300017"],["dc.identifier.pmid","15774050"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/1370"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/53192"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1466-609X"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Uneven distribution of ventilation in acute respiratory distress syndrome"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","67"],["dc.bibliographiccitation.journal","BMC Anesthesiology"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Ball, Lorenzo"],["dc.contributor.author","Brusasco, Claudia"],["dc.contributor.author","Corradi, Francesco"],["dc.contributor.author","Paparo, Francesco"],["dc.contributor.author","Garlaschi, Alessandro"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Pelosi, Paolo"],["dc.date.accessioned","2018-11-07T10:10:04Z"],["dc.date.available","2018-11-07T10:10:04Z"],["dc.date.issued","2016"],["dc.description.abstract","Background: Computed tomography (CT) reconstruction parameters, such as slice thickness and convolution kernel, significantly affect the quantification of hyperaerated parenchyma (VHYPER%). The aim of this study was to investigate the mathematical relation between VHYPER% calculated at different reconstruction settings, in mechanically ventilated and spontaneously breathing patients with different lung pathology. Methods: In this retrospective observational study, CT scans of patients of the intensive care unit and emergency department were collected from two CT scanners and analysed with different kernel-thickness combinations (reconstructions): 1.25 mm soft kernel, 5 mm soft kernel, 5 mm sharp kernel in the first scanner; 2.5 mm slice thickness with a smooth (B41s) and a sharp (B70s) kernel on the second scanner. A quantitative analysis was performed with Maluna (R) to assess lung aeration compartments as percent of total lung volume. CT variables calculated with different reconstructions were compared in pairs, and their mathematical relationship was analysed by using quadratic and power functions. Results: 43 subjects were included in the present analysis. Image reconstruction parameters influenced all the quantitative CT-derived variables. The most relevant changes occurred in the hyperaerated and normally aerated volume compartments. The application of a power correction formula led to a significant reduction in the bias between VHYPER% estimations (p < 0.001 in all cases). The bias in VHYPER% assessment did not differ between lung pathology nor ventilation mode groups (p > 0.15 in all cases). Conclusions: Hyperaerated percent volume at different reconstruction settings can be described by a fixed mathematical relationship, independent of lung pathology, ventilation mode, and type of CT scanner."],["dc.identifier.doi","10.1186/s12871-016-0232-z"],["dc.identifier.isi","000382198600001"],["dc.identifier.pmid","27553378"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13872"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39782"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2253"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Lung hyperaeration assessment by computed tomography: correction of reconstruction-induced bias"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","R129"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","CRITICAL CARE"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Waeschle, Reiner M."],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Hilgers, Reinhard"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Neumann, Peter"],["dc.contributor.author","Quintel, Michael"],["dc.date.accessioned","2018-11-07T11:19:45Z"],["dc.date.available","2018-11-07T11:19:45Z"],["dc.date.issued","2008"],["dc.description.abstract","Introduction The purpose of this study was to assess the relation between glycaemic control and the severity of sepsis in a cohort of patients treated with intensive insulin therapy (IIT). Methods In a prospective, observational study, all patients in the intensive care unit (ICU) (n = 191) with sepsis, severe sepsis or septic shock were treated with IIT (target blood glucose (BG) level 80 to 140 mg/dl instead of strict normoglycaemia). BG values were analysed by calculating mean values, rate of BG values within different ranges, rate of patients experiencing BG values within different levels and standard deviation (SD) of BG values as an index of glycaemic variability. Results The number of patients with hypoglycaemia and hyperglycaemia was highly dependent on the severity of sepsis (critical hypoglycaemia <= 40 mg/dl: sepsis: 2.1%, severe sepsis: 6.0%, septic shock: 11.5%, p = 0.1497; hyperglycaemia: > 140 mg/dl: sepsis: 76.6%, severe sepsis: 88.0%, septic shock: 100%, p = 0.0006; > 179 mg/dl: sepsis: 55.3%, severe sepsis: 73.5%, septic shock: 88.5%, p = 0.0005; > 240 mg/dl: sepsis: 17.0%, severe sepsis: 48.2%, septic shock: 45.9%, p = 0.0011). Multivariate analyses showed a significant association of SD levels with critical hypoglycaemia especially for patients in septic shock (p = 0.0197). In addition, SD levels above 20 mg/dl were associated with a significantly higher mortality rate relative to those with SD levels below 20 mg/dl (24% versus 2.5%, p = 0.0195). Conclusions Patients with severe sepsis and septic shock who were given IIT had a high risk of hypoglycaemia and hyperglycaemia. Among these patients even with a higher target BG level, IIT mandates an increased awareness of the occurrence of critical hypoglycaemia, which is related to the severity of the septic episode."],["dc.identifier.doi","10.1186/cc7097"],["dc.identifier.isi","000261561100017"],["dc.identifier.pmid","18939991"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/4949"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/55364"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1466-609X"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","The impact of the severity of sepsis on the risk of hypoglycaemia and glycaemic variability"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","964"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Pediatric Pulmonology"],["dc.bibliographiccitation.lastpage","975"],["dc.bibliographiccitation.volume","46"],["dc.contributor.author","Tsagogiorgas, Charalambos"],["dc.contributor.author","Alb, Markus"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Meinhardt, Juergen P."],["dc.date.accessioned","2018-11-07T08:51:18Z"],["dc.date.available","2018-11-07T08:51:18Z"],["dc.date.issued","2011"],["dc.description.abstract","Introduction: Total liquid ventilation (TLV) with perfluorocarbons has shown to improve cardiopulmonary function in the injured and immature lung; however there remains controversy over the normal lung. Hemodynamic effects of TLV in the normal lung currently remain undetermined. This study compared changes in cardiopulmonary and circulatory function caused by either liquid or gas tidal volume ventilation. Methods: In a prospective, controlled study, 12 non-injured anesthetized, adult New Zealand rabbits were primarily conventionally gas-ventilated (CGV). After instrumentation for continuous recording of arterial (AP), central venous (CVP), left artrial (LAP), pulmonary arterial pressures (PAP), and cardiac output (CO) animals were randomized into (1) CGV group and (2) TLV group. In the TLV group partial liquid ventilation was initiated with instillation of perfluoroctylbromide (12 ml/kg). After 15 min, TLV was established for 3 hr applying a volume-controlled, pressure-limited, time-cycled ventilation mode using a double-piston configured TLV. Controls (CGV) remained gas-ventilated throughout the experiment. Results: During TLV, heart rate, CO, PAP, MAP, CVP, and LAP as well as derived hemodynamic variables, arterial and mixed venous blood gases, oxygen delivery, PVR, and SVR did not differ significantly compared to CGV. Conclusions: Liquid tidal volumes suitable for long-term TLV in non-injured rabbits do not significantly impair CO, blood pressure, and oxygen dynamics when compared to CGV. Pediatr Pulmonol. 2011;46:964-975. (C) 2011 Wiley-Liss, Inc."],["dc.description.sponsorship","Else-Kroener-Fresenius-Foundation, Bad Homburg, Germany"],["dc.identifier.doi","10.1002/ppul.21461"],["dc.identifier.isi","000295258700004"],["dc.identifier.pmid","21538968"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/21897"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","8755-6863"],["dc.title","Cardiopulmonary Function and Oxygen Delivery During Total Liquid Ventilation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","646"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","IEEE Transactions on Medical Imaging"],["dc.bibliographiccitation.lastpage","652"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Frerichs, Inez"],["dc.contributor.author","Hinz, Jose Maria"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Weisser, G."],["dc.contributor.author","Hahn, G."],["dc.contributor.author","Quintel, M."],["dc.contributor.author","Hellige, Gerhard"],["dc.date.accessioned","2018-11-07T10:28:03Z"],["dc.date.available","2018-11-07T10:28:03Z"],["dc.date.issued","2002"],["dc.description.abstract","The aim of the experiments was to check the feasibility of pulmonary perfusion imaging by functional electrical impedance tomography (FIT) and to compare the FIT findings with electron beam computed tomography (EBCT) scans. In three pigs, a Swan-Ganz catheter was positioned in a pulmonary artery branch and hypertonic saline solution or a radiographic contrast agent were administered as boli through the distal or proximal openings of the catheter. During the administration through the proximal opening, the balloon at the tip of the catheter was either deflated or inflated. The latter case represented a perfusion defect. The series of FIT scans of the momentary distribution of electrical impedance within the chest were obtained during each saline bolus administration at a rate of 13/s. EBCT scans were acquired at a rate of 3.3/s during bolus administrations of the radiopaque contrast material under the same steady-state conditions. The FIT data were used to generate local time-impedance curves and functional FIT images showing the perfusion of a small lung region, both lungs with a perfusion defect and complete both lungs during bolus administration through the distal and proximal catheter opening with an inflated or deflated balloon, respectively. The results indicate that FIT imaging of lung perfusion is feasible when an electrical impedance contrast agent is used."],["dc.identifier.doi","10.1109/TMI.2002.800585"],["dc.identifier.isi","000177190000013"],["dc.identifier.pmid","12166861"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43338"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","0278-0062"],["dc.title","Regional lung perfusion as determined by electrical impedance tomography in comparison with electron beam CT imaging"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","184"],["dc.bibliographiccitation.journal","Journal of Critical Care"],["dc.bibliographiccitation.lastpage","191"],["dc.bibliographiccitation.volume","42"],["dc.contributor.author","Klapsing, Philipp"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Moerer, Onnen"],["dc.date.accessioned","2020-12-10T14:25:02Z"],["dc.date.available","2020-12-10T14:25:02Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1016/j.jcrc.2016.11.001"],["dc.identifier.issn","0883-9441"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72417"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Automatic quantitative computed tomography segmentation and analysis of aerated lung volumes in acute respiratory distress syndrome—A comparative diagnostic study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Physiology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Cressoni, Massimo"],["dc.contributor.author","Lotz, Joachim"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Saager, Leif"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2021-12-01T09:24:03Z"],["dc.date.available","2021-12-01T09:24:03Z"],["dc.date.issued","2021"],["dc.description.abstract","Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically."],["dc.description.abstract","Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically."],["dc.identifier.doi","10.3389/fphys.2021.676118"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94836"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-042X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1126"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Anesthesiology"],["dc.bibliographiccitation.lastpage","1137"],["dc.bibliographiccitation.volume","132"],["dc.contributor.author","Vassalli, Francesco"],["dc.contributor.author","Pasticci, Iacopo"],["dc.contributor.author","Romitti, Federica"],["dc.contributor.author","Duscio, Eleonora"],["dc.contributor.author","Aßmann, David Jerome"],["dc.contributor.author","Grünhagen, Hannah"],["dc.contributor.author","Vasques, Francesco"],["dc.contributor.author","Bonifazi, Matteo"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Macrì, Matteo Maria"],["dc.contributor.author","Giosa, Lorenzo"],["dc.contributor.author","Reupke, Verena"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Hahn, Günter"],["dc.contributor.author","Leopardi, Orazio"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Marini, John J."],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2020-12-10T18:19:48Z"],["dc.date.available","2020-12-10T18:19:48Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1097/ALN.0000000000003189"],["dc.identifier.issn","0003-3022"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/75386"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Does Iso-mechanical Power Lead to Iso-lung Damage?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]